Automobiles extend people's travel range, provide travel convenience to people and improve people's quality of life. With the development and progress of science and technology, driverless vehicles controlled by intelligent systems have become an important trend in future automobile development because they can acquire more driving information than manned vehicles and have higher security. Driverless vehicles use a robot operating system to perform information transmission, and rely on the collaboration of an artificial intelligence module, a visual computing module, a video camera module, a radar sensor module, a laser radar module, and a Global Positioning System (GPS) module, so that the driverless vehicles can automatically and safely travel with no assistance.
However, there remain some insufficiencies in data processing in the existing driverless vehicle. In the existing driverless vehicle, data are typically relayed through the robot operating system, and a location system of the driverless vehicle obtains a location coordinate conversion relationship of the driverless vehicle through various sensors. However, during control process of the driverless vehicle, calculating the current location of the driverless vehicle based on the location coordination conversion relationship is complex, and the information obtained based on the location coordination conversion relationship is limited and simple. In addition, if the data obtained based on the location information are imprecise or false, then the driverless vehicle will judge improperly, thus impeding the driverless vehicle from being accurately controlled.